Etikat

Etikat is a term encountered in several interdisciplinary discussions, without a single formal definition. In some contexts it is used as a coinage describing a method of data tagging that foregrounds ethical considerations. In others it functions as a short form or project name within AI ethics initiatives. Because there is no universal standard, interpretations of etikat vary by community.

The origin of the word is unclear. Some sources describe it as a portmanteau of ethical tagging, while others treat it as an acronym or a proper name. The term appears in online forums, research blogs, and project write-ups, typically in relation to data curation, labeling workflows, and model governance.

In practice, etikat can denote practices that embed privacy, consent, and bias mitigation into labeling processes, metadata schemas, and evaluation criteria. It may include guidelines for documenting provenance, annotator training, impact assessments, and transparency of decision-making. Some discussions frame etikat as a philosophy of categorization that prioritizes responsibility and accountability over speed or sheer scale.

Variants of the concept are described in different circles. Some projects outline an “etikat framework” with defined steps for ethical data labeling, while others refer to an “etikat approach” as a broader mindset about responsible annotation and governance. The term remains most common in niche AI ethics communities and in debates about data stewardship, rather than as a widely adopted standard.

See also: ethics in AI, data labeling, responsible AI, data governance.